1. Field of the Invention
The present invention relates to artificial neural networks. In specific, the present invention relates to electronic learning synapses with spike-dependent plasticity using phase change memory.
2. Background of the Invention
The point of contact between an axon of a neuron and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in conductance of the synapses. The synaptic conductance changes with time as a function of the relative spike times of pre-synaptic and post-synaptic neurons, as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of the two firings is reversed. Furthermore, the change depends on the precise delay between the two events, such that the more the delay, the less the magnitude of change.
Artificial neural networks are computational systems that permit computers to essentially function in a manner analogous to that of biological brains. Artificial neural networks do not generally utilize the traditional digital model of manipulating 0s and 1s. Instead, neural networks create connections between processing elements, which are roughly functionally equivalent to neurons of a biological brain. Artificial neural networks may be comprised of various electronic circuits that are modeled on biological neurons.